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Edison is credited with the creation of the first corporate research lab. In contrast to corporate R&D activity the research labs have pushed the boundaries of science and engineering and brought to the world the transistor, unix and C, fiber optics communication, Ethernet, cellphones, and even evidence for the Big Bang. These labs were famous for hiring the best and brightest scientists and then basically letting them do their own thing. Some were guided by the needs of the corporation, and others were more free spirited. In the 70s and 80s the perceived value of the labs was eroded and as the corporates found themselves out innovated by the new kids on the block such as Microsoft, Intel, and later Google, Apple and others. The new comers did not have the free wheeling research approach and have typically focused their research activity deep in their technical roadmap.

In the past couple of years this has changed and today many of the new kids have their own corporate research labs. However the new labs resemble Edison Menlow Park more than AT&T Bells lab. The new labs tend to focus on grand projects which aim to disrupt existing markets such as autonomous cars, VR and AR replacement for smart phone, global internet reach or even space travel. The similarity to Edison is that the vision of these labs is laid down by the founders or chief executives of the companies and not by free wheeling scientists. Time will tell if the new breed of labs fare better in scientific research or business outcome than the previous ones.

R&D is a delicate balance between optimism and realism. During development things don’t work. it requires tremendous effort to debug problems and make the things work.

The realistic voice lingering in the background seems to speak words of wisdom. Maybe the theory is wrong, maybe there are no bugs just a problem in the assumptions. While this voice is important in quarterly strategic meetings, this voice kills the energy and enthusiasm required to push forward and weed out the bugs.

In projects, especially high risk ones, we need to keep in mind that when things don’t behave as expected it’s not always the problem with the expectations but more often with the things.

Often, when I find myself advising or mentoring entrepreneurs they say ” we are just waiting a few days till we have our important meeting/investor feedback/POC ready” or a wide range of other reasons.

Waiting is always a bad option. It instills passivity and overemphasizes random occurances in the life of a project. Customer leads fizzle out, investors get cold feet, development gets stuck, and in general bright skies become overcast in the blink of an eye.

The proactive option to waiting is to execute a strategic plan which well communicates the project plan to all potential stakeholders. That crucial meeting is one of many. Some occur in parallel, some serially. The outcome of a single meeting, or conversely a project milestone should never be posed as a critical aspect of any plan. Things always fail or come short of expectations. That’s why a long range plan helps all stakeholders maintain perspective even in rough waters.

The increasing capabilities of AI has created concern and even apprehension in the Natural Intelligence community. The concern begins with our limited understanding of evolution; where we assume that all AI will behave like humans and go about destroying mentally inferior species such as the homo sapiens. But then, even if it doesn’t go about annihilating us, it will still take our jobs. So people are writing learned articles about which jobs will still be around when AI is as commonplace as a smart phone.

The doom of driving (e.g. taxi drivers) is predicated to be just around the corner as AI starts driving autonomous cars. Beyond that we imagine smart helpers relieving our every day chores, maybe even doing our shopping and paper work for us.

The common prevailing conception is that low skill jobs would be the most endangered. But in terms of return on investment it seems the greatest impact would actually be in highly skilled jobs. Google already is toying with the idea of creating art with AI but how about engineering. Can we replace engineers with AI ? Or at least augment their capabilities ? and how about inventors ?

Engineering consists of a toolbox of methods to solve a problem and a weight function to determine the worth of one solution compared to another. The challenge is often there is no ideal solution and the optimization consists of trade offs. Moreover, the models are partial, the data collection limited and the decision process fraught with estimations. However, engineers can solve problems because they apply prior knowledge and recognize similarities with past problems. This is exactly where AI excels. If it can play GO, it can probably solve engineering problems.

But can AI invent things ? It probably can. Not only that, since there are already systematic approaches to inventing its surprising that we don’t already have AI that replaces the innovation leads and pathfinders in the R&D. Google should probably be developing AI for inventing and not for doodling. Or maybe its done that already.

The most valuable real estate in presentation is devoted to being polite. That’s certainly a worthwhile trait but realistically, by the time you are proudly being polite people have already forgotten your presentation.

So if you feel compelled to say thank you, than put it in the summary slide.

The ABC of presentation is tell what you are going to tell them (outline) tell them (body) and tell them what you told them (summary).

The last slide standing should summarize your presentation and if you feel compelled add a thank you as the last line to the last slide.

While mosquitos are small in size, their impact on our well being is disporptionaly large. From malaria to zika, or just being a nuisance late at night mosquitoes are a problem waiting to be solved.

The mosquito fight has shifted from wide range chemical warfare, with a broad and severe environmental impact, to local skirmishes. As usual, the individual consumer is left alone in the night, combatting the buzzing sound of a hungry mosquito. Hence it is no wonder that many companies have jumped into the void and offer mosquito traps or repellents.

The amusing fact is that reading the existing studies reveals that these traps or repellents don’t really work. But the traps are an amazing study in marketing products.

Most traps work with UV light. Studies have shown that many insects are attracted to UV light but not mosquitoes. Mosquitoes are actually attracted to heat and CO2. Some traps utilize TiO2 which is a white material commonly used in paint and sun protection lotion and claim when UV light iradiating TiO2 generates CO2.

So there is some science in place. Now placing a trap in open space results in bucket loads of insects. It’s actually impossible to discern mosquitoes from other pests but the sheer amount provides proof the trap is doing its job.

The lesson to be learnt is that while its best to solve the job at hand, selling is sometimes about demonstrating results.

Inventions come from necessity and many times as I find myself spending some time outside I wish for some shade. This got me thinking about a drone assisted solar powered shade system.

Four drones carry a canopy which has a solar panel. The drones position the canopy in the sky to create a shade above the user.

Crazy as it sounds its actually feasible. ( the less physic’s inclined can skip this part). A ten meter square canopy made or organic solar cells should be able to generate around 50 W and yet be light. Furthermore the canopy can be shaped as a wing so the drones don’t actually lift it but only define the angle and speed providing an elevation force for the panel. The drone battery should suffice for the initial elevation but once in place in the sky the sun power and canopy lift will take over.

Positioning is done using a camera on a drone. Once in the sky, the user marks the location to be shaded on his cellphone and the drones do the rest.